deep-learning-book
v2.12.0Study companion and working knowledge base for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, a patterns file and a cheatsheet index the whole book; every chapter carries a 'what changed after 2016' section, and a dedicated delta reference dates the text against 2026 practice with per-claim confidence levels — double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching, and self-supervised learning vindicating Chapter 15 while replacing its methods. Four stdlib-only tools make the book executable: a prerequisite-closed reading-path planner that refuses goals the 2016 book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks the regularization ladder and pushes 'shrink the model' last in the overparameterized regime, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. Deliberately a companion, not a compilation: the book is copyrighted, so nothing here reproduces its text — every chapter file is original synthesis linking to the official free chapter. Use when studying or teaching this book, planning a route through it, or checking whether one of its recommendations is still current.
By Alireza RezvaniLicense: MIT25.1k GitHub starsUpdated 21 hours ago
Directory evidence
- Runtimes
- Claude Code
- Parsed components
- 1 skill or MCP entry
- Source updated
- Aug 26, 2026
- Manifest status
- Canonical path parsed
The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →
Install plugin
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install deep-learning-book@agent-plugin-marketplacePaste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.
The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.
Get the source manually
git clone https://github.com/alirezarezvani/claude-skillsClone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is engineering/deep-learning-book/.
Plugin files
├── .claude-plugin/plugin.json└── skills/deep-learning-book/SKILL.md
Included Skills1
Study companion and working knowledge base for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), read free at deeplearningbook.org. Indexes all 20 chapters, carries a 2016-to-2026 delta layer naming what the book got right, what was superseded (transformers, AdamW, diffusion, double descent) and what still holds, and ships four deterministic tools: a prerequisite-aware reading-path planner, a training-failure diagnostic, a capacity-and-regularization planner, and a parameter/FLOP/activation-memory calculator. Use when studying or teaching this book, planning a route through it, deciding whether a chapter's advice is still current, or translating its math into a training decision. It points at the official chapters — it never reproduces them.
Plugin manifests1
{
"name": "deep-learning-book",
"description": "Study companion and working knowledge base for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, a patterns file and a cheatsheet index the whole book; every chapter carries a 'what changed after 2016' section, and a dedicated delta reference dates the text against 2026 practice with per-claim confidence levels — double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching, and self-supervised learning vindicating Chapter 15 while replacing its methods. Four stdlib-only tools make the book executable: a prerequisite-closed reading-path planner that refuses goals the 2016 book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks the regularization ladder and pushes 'shrink the model' last in the overparameterized regime, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. Deliberately a companion, not a compilation: the book is copyrighted, so nothing here reproduces its text — every chapter file is original synthesis linking to the official free chapter. Use when studying or teaching this book, planning a route through it, or checking whether one of its recommendations is still current.",
"version": "2.12.0",
"author": {
"name": "Alireza Rezvani",
"url": "https://alirezarezvani.com"
},
"homepage": "https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book",
"repository": "https://github.com/alirezarezvani/claude-skills",
"license": "MIT",
"skills": [
"./skills/deep-learning-book"
]
}For maintainers
If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.
[deep-learning-book on Agent Plugins Marketplace](https://pluginsmp.com/plugins/deep-learning-book)